Final Data Plotting

Class 25

Exposition and Plot Description

The data set used here is from Our World in Data on number of deaths by risk factor worldwide. Of these data, I selected the data for the United States and plotted the change in deaths due to air pollution over time. I first read in the data from a CSV file and filtered it to select only data from the United States. For the plot, I made a time series plot of the yearly deaths due to air pollution in the United States between 1970 and 2017. It is interesting to see how deaths due to air pollution have increased since 1970 as emissions began to rise, but have begun to lower since then as the Untied States has introduced policies aimed at curbing climate change in more recent times. I also adjusted the scale of the x-axis to display years in increments of 5.

Code

library(tidyverse)

# read in the "number-of-deaths-by-risk-factor.csv" and filter for data from the United States
airPollutionData = read_csv('/Users/adam/Documents/School Files/University of Virginia/Second Year/Spring Semester/DS3003/finalProject/finalDataPlotting/number-of-deaths-by-risk-factor.csv') %>% filter(`Code`=="USA")

# make a ggplot time series with the year on the x-axis and deaths by air pollution on the y-axis
p1 <- ggplot() + geom_line(data=airPollutionData, aes(x=`Year`, y=`Deaths - Air pollution - Sex: Both - Age: All Ages (Number)`, group=1), color='#FF0000') +
  # set the plot title and y-axis title
  labs(title='Air Pollution Deaths in the United States between 1990 and 2017',
       y='Deaths by Air Pollution') +
  # adjust the frequency of the tick marks on the x-axis to every 5 years
  scale_x_continuous(breaks=seq(1990, 2017, 5))

Plot

Class 26

Exposition and Plot Description

The data used here is from Kaggle where it was collected from the OECD. Of these data, I selected the data for the year 2011 as that was the most commonly occurring year in the data. The plot below shows the share of one person households in various different countries in 2011. To start, I first read in the data from the “one-person-households.csv” file and filtered for datapoints in the year 2011. I used Plotly to create a bar plot with one bar corresponding to each country’s 2011 share of one-person households. I also changed the color of the bars to red, allowed each country’s exact value to be identified and changed the plot height to 1000 so that all of the countries would fit. Using the layout function, I also created a title for the plot as well as the x and y axes.

Code

library(tidyverse)
library(plotly)

# read in the "one-person-households" CSV file and select data from the year 2011
onePersonHouseholdData = read.csv('/Users/adam/Documents/School Files/University of Virginia/Second Year/Spring Semester/DS3003/finalProject/finalDataPlotting/one-person-households.csv') %>% filter(`Year`==2011)

p2 <- onePersonHouseholdData %>% 
  plot_ly(
    x=~`Share.of.one.person.households`, # select x-axis data
    y=~`Entity`, # select y-axis data
    marker=list(color='#FF0000'),
    text=~`Share.of.one.person.households`, # allow the exact share of one person households to be identified when hovering over each bar
    hoverinfo='text',
    type='bar', # specify the type of plot
    height=1000) %>% # specify the plot height so all country names are shown
      layout(title='Share of One Person Households in Different Countries in 2011',
             xaxis=list(title='Share of One Person Households'), # add title for x-axis
             yaxis=list(title='')) # add blank title for the y-axis

Plot

Class 27

Exposition and Map Description

The data used here is taken from Statistica and is about broadcasting payments received by various soccer teams in 2019 and 2020. Of these data, I selected the data for Liverpool Football Club and created a pie chart to represent the percentages of payments from different broadcasting sources it received. To create the the plot, I first created vectors to contain the data points as well as the different categories of income. I then divided each data point by the total to create a percentage. I then displayed the plot using a Plotly Pie Chart.

Code

library(tidyverse)
library(readxl)

# read in the "statistic_id240912_premier-league-broadcasting-payments-to-clubs-2019-20" excel file
broadcastingData=readxl::read_excel('/Users/adam/Documents/School Files/University of Virginia/Second Year/Spring Semester/DS3003/finalProject/finalDataPlotting/statistic_id240912_premier-league-broadcasting-payments-to-clubs-2019-20.xlsx') 

# create payment types vector
broadcastingData[2,]  %>% select(-1) -> paymentTypes

# convert to vector
as.character(paymentTypes[1,]) -> paymentTypesVec

# create data points vector
broadcastingData %>% filter(`Premier League broadcasting payments to clubs 2019/20`== 'Liverpool FC') -> nums

nums[-1] -> broadcastingVec

# convert to vector
as.numeric(broadcastingVec) -> broadcastingVec

# create percentages by dividing each data point by the total
broadcastingVec/sum(broadcastingVec) -> broadcastingVec

# creata a pie chart using Plotly
p3 <- plot_ly(broadcastingData, labels=~paymentTypesVec, values=~broadcastingVec, type='pie')
p3 <- p3 %>% layout(title='Percentage of Broadcasting Payments Received by Liverpool FC in 2019/2020',
         xaxis = list(showgrid=FALSE, zeroline=FALSE, showticklabels=FALSE),
         yaxis = list(showgrid=FALSE, zeroline=FALSE, showticklabels=FALSE))

Plot